Upsampling vs oversampling machine learning

1. What is upsampling in machine learning?
2. Is oversampling good in machine learning?
3. Is it better to oversample or Undersample?
4. What is the purpose of oversampling in machine learning?

What is upsampling in machine learning?

Upsampling or Oversampling refers to the technique to create artificial or duplicate data points or of the minority class sample to balance the class label. There are various oversampling techniques that can be used to create artificial data points.

Is oversampling good in machine learning?

Random Oversampling

For Machine Learning algorithms affected by skewed distribution, such as artificial neural networks and SVMs, this is a highly effective technique.

Is it better to oversample or Undersample?

Oversampling methods duplicate or create new synthetic examples in the minority class, whereas undersampling methods delete or merge examples in the majority class. Both types of resampling can be effective when used in isolation, although can be more effective when both types of methods are used together.

What is the purpose of oversampling in machine learning?

Over sampling is used when the amount of data collected is insufficient. A popular over sampling technique is SMOTE (Synthetic Minority Over-sampling Technique), which creates synthetic samples by randomly sampling the characteristics from occurrences in the minority class.